{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-09-27T13:41:08.275964Z",
     "start_time": "2025-09-27T13:40:59.920973Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "\n",
    "def polynomial(x, degree):\n",
    "    \"\"\"构成多项式，返回 [x^1,x^2,x^3,...,x^n]\"\"\"\n",
    "    return np.hstack([x ** i for i in range(1, degree + 1)])\n",
    "\n",
    "\n",
    "# 生成随机数据\n",
    "X = np.linspace(-3, 3, 300).reshape(-1, 1)\n",
    "y = np.sin(X) + np.random.uniform(-0.5, 0.5, 300).reshape(-1, 1)\n",
    "\n",
    "fig, ax = plt.subplots(1, 3, figsize=(15, 4))\n",
    "ax[0].plot(X, y, \"yo\")\n",
    "ax[1].plot(X, y, \"yo\")\n",
    "ax[2].plot(X, y, \"yo\")\n",
    "\n",
    "# 划分训练集和测试集\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 创建线性回归模型\n",
    "model = LinearRegression()\n",
    "\n",
    "# 欠拟合\n",
    "x_train1 = x_train\n",
    "x_test1 = x_test\n",
    "# 模型训练\n",
    "model.fit(x_train1, y_train)\n",
    "# 预测\n",
    "y_pred1 = model.predict(x_test1)\n",
    "\n",
    "# 绘制曲线\n",
    "ax[0].plot(np.array([-3]), model.predict(np.array([[-3], [3]])), \"c\")\n",
    "ax[0].text(-3, 1, f\"测试集均方误差：{mean_squared_error(y_test, y_pred1):.4f}\")\n",
    "ax[0].text(-3, 1.3, f\"训练集均方误差：{mean_squared_error(y_train, model.predict(x_train1)):.4f}\")\n",
    "\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (1,) and (2, 1)",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[1], line 40\u001B[0m\n\u001B[1;32m     37\u001B[0m y_pred1 \u001B[38;5;241m=\u001B[39m model\u001B[38;5;241m.\u001B[39mpredict(x_test1)\n\u001B[1;32m     39\u001B[0m \u001B[38;5;66;03m# 绘制曲线\u001B[39;00m\n\u001B[0;32m---> 40\u001B[0m ax[\u001B[38;5;241m0\u001B[39m]\u001B[38;5;241m.\u001B[39mplot(np\u001B[38;5;241m.\u001B[39marray([\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m3\u001B[39m]), model\u001B[38;5;241m.\u001B[39mpredict(np\u001B[38;5;241m.\u001B[39marray([[\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m3\u001B[39m], [\u001B[38;5;241m3\u001B[39m]])), \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mc\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m     41\u001B[0m ax[\u001B[38;5;241m0\u001B[39m]\u001B[38;5;241m.\u001B[39mtext(\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m3\u001B[39m, \u001B[38;5;241m1\u001B[39m, \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m测试集均方误差：\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mmean_squared_error(y_test,\u001B[38;5;250m \u001B[39my_pred1)\u001B[38;5;132;01m:\u001B[39;00m\u001B[38;5;124m.4f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m     42\u001B[0m ax[\u001B[38;5;241m0\u001B[39m]\u001B[38;5;241m.\u001B[39mtext(\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m3\u001B[39m, \u001B[38;5;241m1.3\u001B[39m, \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m训练集均方误差：\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mmean_squared_error(y_train,\u001B[38;5;250m \u001B[39mmodel\u001B[38;5;241m.\u001B[39mpredict(x_train1))\u001B[38;5;132;01m:\u001B[39;00m\u001B[38;5;124m.4f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n",
      "File \u001B[0;32m~/opt/anaconda3/lib/python3.13/site-packages/matplotlib/axes/_axes.py:1777\u001B[0m, in \u001B[0;36mAxes.plot\u001B[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001B[0m\n\u001B[1;32m   1534\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m   1535\u001B[0m \u001B[38;5;124;03mPlot y versus x as lines and/or markers.\u001B[39;00m\n\u001B[1;32m   1536\u001B[0m \n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m   1774\u001B[0m \u001B[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001B[39;00m\n\u001B[1;32m   1775\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m   1776\u001B[0m kwargs \u001B[38;5;241m=\u001B[39m cbook\u001B[38;5;241m.\u001B[39mnormalize_kwargs(kwargs, mlines\u001B[38;5;241m.\u001B[39mLine2D)\n\u001B[0;32m-> 1777\u001B[0m lines \u001B[38;5;241m=\u001B[39m [\u001B[38;5;241m*\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_get_lines(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;241m*\u001B[39margs, data\u001B[38;5;241m=\u001B[39mdata, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)]\n\u001B[1;32m   1778\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m line \u001B[38;5;129;01min\u001B[39;00m lines:\n\u001B[1;32m   1779\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39madd_line(line)\n",
      "File \u001B[0;32m~/opt/anaconda3/lib/python3.13/site-packages/matplotlib/axes/_base.py:297\u001B[0m, in \u001B[0;36m_process_plot_var_args.__call__\u001B[0;34m(self, axes, data, return_kwargs, *args, **kwargs)\u001B[0m\n\u001B[1;32m    295\u001B[0m     this \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m args[\u001B[38;5;241m0\u001B[39m],\n\u001B[1;32m    296\u001B[0m     args \u001B[38;5;241m=\u001B[39m args[\u001B[38;5;241m1\u001B[39m:]\n\u001B[0;32m--> 297\u001B[0m \u001B[38;5;28;01myield from\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_plot_args(\n\u001B[1;32m    298\u001B[0m     axes, this, kwargs, ambiguous_fmt_datakey\u001B[38;5;241m=\u001B[39mambiguous_fmt_datakey,\n\u001B[1;32m    299\u001B[0m     return_kwargs\u001B[38;5;241m=\u001B[39mreturn_kwargs\n\u001B[1;32m    300\u001B[0m )\n",
      "File \u001B[0;32m~/opt/anaconda3/lib/python3.13/site-packages/matplotlib/axes/_base.py:494\u001B[0m, in \u001B[0;36m_process_plot_var_args._plot_args\u001B[0;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001B[0m\n\u001B[1;32m    491\u001B[0m     axes\u001B[38;5;241m.\u001B[39myaxis\u001B[38;5;241m.\u001B[39mupdate_units(y)\n\u001B[1;32m    493\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m x\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m0\u001B[39m] \u001B[38;5;241m!=\u001B[39m y\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m0\u001B[39m]:\n\u001B[0;32m--> 494\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mx and y must have same first dimension, but \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    495\u001B[0m                      \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mhave shapes \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mx\u001B[38;5;241m.\u001B[39mshape\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m and \u001B[39m\u001B[38;5;132;01m{\u001B[39;00my\u001B[38;5;241m.\u001B[39mshape\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m    496\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m x\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m2\u001B[39m \u001B[38;5;129;01mor\u001B[39;00m y\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m2\u001B[39m:\n\u001B[1;32m    497\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mx and y can be no greater than 2D, but have \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    498\u001B[0m                      \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mshapes \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mx\u001B[38;5;241m.\u001B[39mshape\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m and \u001B[39m\u001B[38;5;132;01m{\u001B[39;00my\u001B[38;5;241m.\u001B[39mshape\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n",
      "\u001B[0;31mValueError\u001B[0m: x and y must have same first dimension, but have shapes (1,) and (2, 1)"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 1
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
